


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE     :			List[str]        =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :			int        =							{"""vocab_file""": """spm_char.model"""}

_SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							{
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

_SCREAMING_SNAKE_CASE     :			Dict        =							{
    """microsoft/speecht5_asr""": 1_024,
    """microsoft/speecht5_tts""": 1_024,
    """microsoft/speecht5_vc""": 1_024,
}



class        A     (		snake_case_	):







     '''simple docstring'''







     lowerCamelCase :						List[str]            =   VOCAB_FILES_NAMES
     lowerCamelCase :						List[str]            =   PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase :						int            =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase :						Any            =   ["""input_ids""", """attention_mask"""]

     def __init__(				self    :							List[Any]			,				_UpperCamelCase    :							Optional[Any]			,				_UpperCamelCase    :							Union[str, Any]="<s>"			,				_UpperCamelCase    :							int="</s>"			,				_UpperCamelCase    :							Any="<unk>"			,				_UpperCamelCase    :							Optional[int]="<pad>"			,				_UpperCamelCase    :							Union[str, Any] = None			,				**_UpperCamelCase    :							str			,				):
      _lowercase:       Dict						=				{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_UpperCamelCase			,				eos_token=_UpperCamelCase			,				unk_token=_UpperCamelCase			,				pad_token=_UpperCamelCase			,				sp_model_kwargs=self.sp_model_kwargs			,				**_UpperCamelCase			,				)

      _lowercase:       List[Any]						=				vocab_file

      _lowercase:       Tuple						=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(_UpperCamelCase)

     @property
     def 					UpperCAmelCase__       (				self    :							str):
      return self.sp_model.get_piece_size()

     def 					UpperCAmelCase__       (				self    :							Dict):
      _lowercase:       Tuple						=				{self.convert_ids_to_tokens(_UpperCamelCase): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

     def __getstate__(				self    :							List[Any]):
      _lowercase:       Dict						=				self.__dict__.copy()
      _lowercase:       Optional[Any]						=				None
      return state

     def __setstate__(				self    :							Optional[Any]			,				_UpperCamelCase    :							int):
      _lowercase:       List[Any]						=				d

      # for backward compatibility
      if not hasattr(self			,				"sp_model_kwargs"):
       _lowercase:       Tuple						=				{}

      _lowercase:       List[str]						=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							str):
      return self.sp_model.encode(_UpperCamelCase			,				out_type=_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							int			,				_UpperCamelCase    :							Optional[int]):
      return self.sp_model.piece_to_id(_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							str):
      _lowercase:       Union[str, Any]						=				self.sp_model.IdToPiece(_UpperCamelCase)
      return token

     def 					UpperCAmelCase__       (				self    :							str			,				_UpperCamelCase    :							List[Any]):
      _lowercase:       Any						=				[]
      _lowercase:       Union[str, Any]						=				""
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        out_string += self.sp_model.decode(_UpperCamelCase) + token
        _lowercase:       Optional[Any]						=				[]
       else:
        current_sub_tokens.append(_UpperCamelCase)
      out_string += self.sp_model.decode(_UpperCamelCase)
      return out_string.strip()

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							Optional[Any]			,				_UpperCamelCase    :							List[str]=None):
      if token_ids_a is None:
       return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]

     def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							Tuple = None			,				_UpperCamelCase    :							str = False):
      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_UpperCamelCase			,				token_ids_a=_UpperCamelCase			,				already_has_special_tokens=_UpperCamelCase)

      _lowercase:       Dict						=				[1]
      if token_ids_a is None:
       return ([0] * len(_UpperCamelCase)) + suffix_ones
      return ([0] * len(_UpperCamelCase)) + ([0] * len(_UpperCamelCase)) + suffix_ones






     def 					UpperCAmelCase__       (				self    :							str			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Union[str, Any] = None):
      if not os.path.isdir(_UpperCamelCase):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory")
       return
      _lowercase:       Optional[int]						=				os.path.join(
          _UpperCamelCase			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_UpperCamelCase) and os.path.isfile(self.vocab_file):
       copyfile(self.vocab_file			,				_UpperCamelCase)
      elif not os.path.isfile(self.vocab_file):
       with open(_UpperCamelCase			,				"wb") as fi:
        _lowercase:       int						=				self.sp_model.serialized_model_proto()
        fi.write(_UpperCamelCase)

      return (out_vocab_file,)


"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE__:int									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use DeformableDetrImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
